Research On Maximum Power Tracking And State Of Charge Estimation For Solar Electric Vehicle Battery Management System

To overcome the global greenhouse effect,improve environmental pollution,and develop clean green energy has become an inevitable trend in the world.Solar electric vehicles have gradually become the research focus of new energy vehicles due to their zero emissions and pollution-free eharacteristics.With the continuous development and application of solar electric vehicles,the research on key technologies of solar electric vehicles is becoming more and more important.The Battery Management System(BMS)is a bridge connecting the entire vehicle,battery pack and photovoltaic panels.Its performance is of great significance for improving photovoltaic power generation efficiency,extending the life of the battery pack and increasing the mileage of the vehicle.In order to make full use of the electric energy generated by solar electric vehicle photovoltaic cells,the operation of photovoltaic power generation systems must quickly and accurately track the maximum power point of photovoltaic power generation.The conventional improved disturbance observation method oscillates around the maximum power point,and the setting of the fixed step size is difficult to balance the tracking accuracy and speed.In order to quickly and accurately track the maximum power point and solve the contradiction between tracking accuracy and response speed,this paper proposes a variable step size fuzzy control method based on power prediction.By introducing the power threshold point on the basis of the power prediction method,the algorithm is fast,and the variable step size is introduced.The fuzzy control is used to realize the real-time control of the variable step size,and finally the maximum power point tracking is completed.The simulation results show that the maximum power tracking accuracy of the variable step size fuzzy control method based on power prediction is 97.7%,the tracking efficiency is 97.255%,and the tracking speed is obviously better than the fast convergence method.The experimental results show that the actual maximum power tracking and tracking accuracy of the variable step size fuzzy control method based on power prediction is 95.23%,and the tracking efficiency is 95.725%.Therefore,the paper proposes that the power prediction variable step size fuzzy control method can quickly and accurately track the maximum power point and maintain the dynamic performance and steady state performance of the system.The state of charge represents the amount of remaining battery power.Accurate SOC estimation methods are important for preventing overcharge,overdischarge and battery life.In order to effectively improve the accuracy of SOC estimation,and overcome the problem that the adaptive Kalman filter algorithm relies too much on the accurate battery model and the system noise must obey the Gaussian white noise distribution,a genetic algorithm adaptive unscented Kalman filter(GA-AUKF)is proposed.Joint estimation method for battery state of charge.Firstly,in order to accurately simulate the working mechanism of the battery and express the relationship between the main parameters of the battery,this paper based on the second-order equivalent circuit model,using the forgetting factor recursive least squares method(FFRLS)to identify the model parameters online;secondly,to weaken the system The influence of noise and measurement noise on SOC estimation aecuracy i5 optimized by genetic algorithm for adaptive unscented Kalman filter noise matrix.Finally,FFRLS and GA-AUKF are combined to perform SOC estimation.The results show that the estimation accuracy of the proposed algorithm is better than that of AUKF,which can effectively reduce the influence of filter noise covariance and improve the estimation accuracy.The estimation error is within 1%.The paper proposes that the power prediction variable step size fuzzy control method can improve the maximum power tracking accuracy and ensure the tracking efficiency.Secondly,the paper proposes that the GA-AUKF joint estimation method solves the contradiction between the complexity of the power battery model and the estimation accuracy,and provides a theoretical basis for the online joint estimation of solar SOC battery SOC.The improvement of maximum power tracking performance and SOC estimation accuracy has laid a solid foundation for the subsequent research of solar electric vehicle battery management system.